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An iterative problem-driven scenario reduction framework for stochastic optimization with conditional value-at-risk
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DOI：10.1016/j.epsr.2026.113730.png)
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摘要
En 中文
• 一种迭代的问题驱动场景缩减框架。
• 嵌入风险规避优化结构以增强尾部风险表征。
• 将场景分割和选择表述为混合整数规划问题。
• 以低于1%的最优性间隙实现近最优解。
• 表现优于现有的场景缩减方法。
Keyword:
Conditional value-at-risk
Problem-driven
Risk-averse
Scenario reduction
Stochastic optimization
期刊
IF:
4.2
论文数:
1.2W
被引数:
2.2W
机构
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